Implementation of VGG-A by chainer
git clone https://github.com/nutszebra/vgg.git
cd vgg
git submodule init
git submodule update
python main.py -p ./ -g 0
All hyperparameters and network architecture are the same as in [1] except for data-augmentation.
- Data augmentation
Train: Pictures are randomly resized in the range of [256, 512], then 224x224 patches are extracted randomly and are normalized locally. Horizontal flipping is applied with 0.5 probability.
Test: Pictures are resized to 384x384, then they are normalized locally. Single image test is used to calculate total accuracy.
| network | depth | total accuracy (%) |
|---|---|---|
| my implementation | 11 | 92.1 |
Very Deep Convolutional Networks for Large-Scale Image Recognition [1]